Senior Engineer, Machine Learning/AI Ops

National Debt Relief, LLC.•,

4d•$150,500 - $173,000•Remote

About The Position

National Debt Relief (NDR) is seeking a Senior Engineer, ML & AI Ops to build and operate the AWS platform supporting our machine learning models, internal AI tools, and event-driven integrations. This role partners closely with Data Science, Data Engineering, Analytics, Product, and other engineering teams. NDR’s ML platform is being built out on AWS now, and this role extends it. The role spans model training and inference, real-time APIs, change data capture, and secure integrations between internal and third-party systems. The ideal candidate combines deep AWS and infrastructure-as-code experience with strong software engineering fundamentals. They have built reliable production services, integrations, and ML systems where security, observability, and auditability are essential.

Requirements

6+ years in MLOps, platform engineering, DevOps/SRE, or backend engineering, with significant experience supporting ML, data, or event-driven production workloads.
Bachelor’s degree in a relevant technical field, required.
Deep, hands-on AWS experience designing and operating production workloads across compute, messaging, and security.
Strong experience building event-driven integrations with third-party APIs, including change data capture.
Experience with Terraform and infrastructure-as-code across multi-environment production systems.
Hands-on experience deploying and operating ML models, including batch and real-time serving, versioning, and monitoring.
Experience with containers, CI/CD, and safe production deployment practices.
Experience operating PostgreSQL for production services, including schema migrations, indexing, and correctness under concurrent writes.
Strong professional Python experience building and operating production APIs, services, and integrations.
High autonomy and strong communication skills, with the ability to operate from desired outcomes rather than prescribed approaches and influence platform engineering standards.
Computer competency and ability to work with a computer.
Prioritize multiple tasks and projects simultaneously.
Exceptional written and verbal communication skills.
Punctuality expected, ready to report to work on a consistent basis.
Attain and maintain high performance expectations on a monthly basis.
Work in a fast-paced, high-volume setting.
Use and navigate multiple computer systems with exceptional multi-tasking skills.
Remain calm and professional during difficult discussions.
Take constructive feedback.
Available for full-time position.

Nice To Haves

Experience operating or securing internal LLM-based or agentic systems.
Experience with ML observability, experiment tracking, or drift detection tooling.
Hands-on Snowflake experience supporting ML or production data workloads.
Experience in financial services, fintech, or another regulated industry.

Responsibilities

Design, build, and operate the AWS infrastructure and services behind NDR’s ML models, internal AI tools, and event-driven integrations.
Build and operate event-driven integrations and CDC pipelines between internal and third-party systems, including Salesforce and platforms such as Braze.
Design integrations for reliability and recoverability, including idempotency, replay, schema evolution, and out-of-order events.
Build and maintain containerized inference APIs and backend services with secure, versioned interfaces and production-grade performance.
Manage infrastructure in Terraform across multiple AWS accounts and environments using reusable modules and CI-gated workflows.
Build ML workflows for training, retraining, scoring, and inference, and partner with Data Science to create repeatable paths from experiment to production.
Implement observability across ML systems, AI tools, and integration services, including logging, metrics, alerting, and model monitoring.
Support secure operation of internal LLM and agentic systems, including Amazon Bedrock, with appropriate access controls, auditing, and data protections.
Partner with Data Engineering on Snowflake datasets, data access, and governance supporting ML and operational workflows.
Lead incident response, participate in on-call, and contribute runbooks, reusable patterns, and platform engineering standards.